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Pedigree Analysis

Pedigree Analysis

Pedigree Analysis

Pedigree Analysis

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

Incomplete Dominance

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.

Epistasis Analysis

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

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Related Experiment Video

Updated: Jul 7, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

An approach for cutting large and complex pedigrees for linkage analysis.

Fan Liu¹, Anatoliy Kirichenko, Tatiana I Axenovich

¹Department of Epidemiology & Biostatistics, Erasmus MC, Rotterdam, The Netherlands.

European Journal of Human Genetics : EJHG

|February 28, 2008

Summary

This study introduces a novel pedigree-splitting method for computational linkage analysis. The algorithm efficiently identifies subpedigrees, improving genetic analysis for large, complex families and rare mutations.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

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Last Updated: Jul 7, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

Genetics
Computational Biology
Bioinformatics

Background:

Linkage analysis in large pedigrees presents significant computational challenges, limiting the application of standard algorithms like Lander-Green-Kruglyak.
Existing methods often require splitting large pedigrees into smaller, manageable units for analysis.

Purpose of the Study:

To develop and present a novel pedigree-splitting method to address computational limitations in linkage analysis.
To identify subpedigrees containing the maximum number of subjects of interest (e.g., patients) who share a common ancestor within a specified bit-size limit.

Main Methods:

A new pedigree-splitting algorithm was developed, prioritizing subpedigrees with the highest density of subjects of interest.
The method was compared against the maximum clique partitioning approach using a large, complex human pedigree from a genetically isolated Dutch population.
The pedigree included 50 patients with Alzheimer's disease.

Main Results:

The proposed pedigree-splitting method assigned more patients to subpedigrees compared to the clique partitioning method under a bit-size constraint.
The method demonstrated particular effectiveness in splitting deep pedigrees with scattered subjects of interest across recent generations.
The algorithm successfully handled complex genealogic connections and distant relationships.

Conclusions:

The developed pedigree-splitting algorithm and associated software (PedCut) can significantly enhance genome-wide linkage scans for rare mutations in large pedigrees.
This approach is especially beneficial for genetically isolated populations and complex family structures.
The software facilitates more efficient and comprehensive genetic analyses in challenging pedigree datasets.